Anomaly detection through header field entropy

ABSTRACT

An approach for detecting anomalous flows in a network using header field entropy. This can be useful in detecting anomalous or malicious traffic that may attempt to “hide” or inject itself into legitimate flows. A malicious endpoint might attempt to send a control message in underutilized header fields or might try to inject illegitimate data into a legitimate flow. These illegitimate flows will likely demonstrate header field entropy that is higher than legitimate flows. Detecting anomalous flows using header field entropy can help detect malicious endpoints.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/173,489 filed on Jun. 3, 2016, which claims priority to U.S. Provisional Patent Application Ser. No. 62/171,899 filed on Jun. 5, 2015, the contents of which are incorporated by reference in their entireties.

TECHNICAL FIELD

The present technology pertains to network security and more specifically to detecting anomalous flows using header field entropy.

BACKGROUND

Malicious endpoints can use a variety of techniques to infiltrate a network and send malicious code to an endpoint. For example, a malicious endpoint might try to inject illegitimate traffic into a legitimate flow. It might do this by trying to guess the legitimate flow's current sequence number. Another way that a malicious endpoint might try to infiltrate the network is it might attempt to send a command signal to an infected endpoint. It can do this by sending a “port knock” or an unused header field. In many networks, some or all of this header field information is inaccessible to a security program.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only example embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example network traffic monitoring system according to some example embodiments;

FIG. 2 illustrates an example network environment according to some example embodiments;

FIG. 3 illustrates an example method according to some example embodiments;

FIG. 4 illustrates an example graph according to some example embodiments;

FIG. 5 illustrates an example graph according to some example embodiments; and

FIGS. 6A and 6B illustrate example system embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

An approach for detecting anomalous flows in a network using header field entropy. This can be useful in detecting anomalous or malicious traffic that may attempt to “hide” or inject itself into legitimate flows. A malicious endpoint might attempt to send a control message in underutilized header fields or might try to inject illegitimate data into a legitimate flow. These illegitimate flows will likely demonstrate header field entropy that is higher than legitimate flows. Detecting anomalous flows using header field entropy can help detect malicious endpoints.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure.

The disclosed technology addresses the need in the art for detecting anomalous flows using header field entropy.

FIG. 1 shows an example network traffic monitoring system 100 according to some example embodiments. Network traffic monitoring system 100 can include configuration and image manager 102, sensors 104, external data sources 106, collectors 108, analytics module 110, policy engine 112, and presentation module 116. These modules may be implemented as hardware and/or software components. Although FIG. 1 illustrates an example configuration of the various components of network traffic monitoring system 100, those of skill in the art will understand that the components of network traffic monitoring system 100 or any system described herein can be configured in a number of different ways and can include any other type and number of components. For example, sensors 104 and collectors 108 can belong to one hardware and/or software module or multiple separate modules. Other modules can also be combined into fewer components and/or further divided into more components.

Configuration and image manager 102 can provision and maintain sensors 104. In some example embodiments, sensors 104 can reside within virtual machine images, and configuration and image manager 102 can be the component that also provisions virtual machine images.

Configuration and image manager 102 can configure and manage sensors 104. When a new virtual machine (VM) is instantiated or when an existing VM is migrated, configuration and image manager 102 can provision and configure a new sensor on the physical server hosting the VM. In some example embodiments configuration and image manager 102 can monitor the health of sensors 104. For instance, configuration and image manager 102 may request status updates or initiate tests. In some example embodiments, configuration and image manager 102 can also manage and provision the virtual machines themselves.

In some example embodiments, configuration and image manager 102 can verify and validate sensors 104. For example, sensors 104 can be provisioned a unique ID that is created using a one-way hash function of its basic input/output system (BIOS) universally unique identifier (UUID) and a secret key stored on configuration and image manager 102. This UUID can be a large number that is difficult for an imposter sensor to guess. In some example embodiments, configuration and image manager 102 can keep sensors 104 up to date by installing new versions of their software and applying patches. Configuration and image manager 102 can obtain these updates automatically from a local source or the Internet.

Sensors 104 can reside on nodes of a data center network (e.g., virtual partition, hypervisor, physical server, switch, router, gateway, other network device, other electronic device, etc.). In general, a virtual partition may be an instance of a virtual machine (VM) (e.g., VM 104 a), sandbox, container (e.g., container 104 c), or any other isolated environment that can have software operating within it. The software may include an operating system and application software. For software running within a virtual partition, the virtual partition may appear to be a distinct physical server. In some example embodiments, a hypervisor (e.g., hypervisor 104 b) may be a native or “bare metal” hypervisor that runs directly on hardware, but that may alternatively run under host software executing on hardware. Sensors 104 can monitor communications to and from the nodes and report on environmental data related to the nodes (e.g., node IDs, statuses, etc.). Sensors 104 can send their records over a high-speed connection to collectors 108 for storage. Sensors 104 can comprise a piece of software (e.g., running on a VM, container, virtual switch, hypervisor, physical server, or other device), an application-specific integrated circuit (ASIC) (e.g., a component of a switch, gateway, router, standalone packet monitor, or other network device including a packet capture (PCAP) module or similar technology), or an independent unit (e.g., a device connected to a network device's monitoring port or a device connected in series along a main trunk of a datacenter). It should be understood that various software and hardware configurations can be used as sensors 104. Sensors 104 can be lightweight, thereby minimally impeding normal traffic and compute resources in a datacenter. Sensors 104 can “sniff” packets being sent over its host network interface card (NIC) or individual processes can be configured to report traffic to sensors 104. This sensor structure allows for robust capture of granular (i.e., specific) network traffic data from each hop of data transmission.

As sensors 104 capture communications, they can continuously send network traffic and associated data to collectors 108. The network traffic data can relate to a packet, a collection of packets, a flow, a group of flows, etc. The associated data can include details such as the VM BIOS ID, sensor ID, associated process ID, associated process name, process user name, sensor private key, geo-location of a sensor, environmental details, etc. The network traffic data can include information describing the communication on all layers of the Open Systems Interconnection (OSI) model. For example, the network traffic data can include signal strength (if applicable), source/destination media access control (MAC) address, source/destination internet protocol (IP) address, protocol, port number, encryption data, requesting process, a sample packet, etc.

In some example embodiments, sensors 104 can preprocess network traffic data before sending to collectors 108. For example, sensors 104 can remove extraneous or duplicative data or they can create a summary of the data (e.g., latency, packets and bytes sent per flow, flagged abnormal activity, etc.). In some example embodiments, sensors 104 can be configured to only capture certain types of connection information and disregard the rest. Because it can be overwhelming for a system to capture every packet in a network, in some example embodiments, sensors 104 can be configured to capture only a representative sample of packets (e.g., every 1,000th packet or other suitable sample rate).

Sensors 104 can send network traffic data to one or multiple collectors 108. In some example embodiments, sensors 104 can be assigned to a primary collector and a secondary collector. In other example embodiments, sensors 104 are not assigned a collector, but can determine an optimal collector through a discovery process. Sensors 104 can change where they send their network traffic data if their environments change, such as if a certain collector experiences failure or if a sensor is migrated to a new location and becomes closer to a different collector. In some example embodiments, sensors 104 can send different types of network traffic data to different collectors. For example, sensors 104 can send network traffic data related to one type of process to one collector and network traffic data related to another type of process to another collector.

Collectors 108 can serve as a repository for the data recorded by sensors 104. In some example embodiments, collectors 108 can be directly connected to a top of rack switch. In other example embodiments, collectors 108 can be located near an end of row switch. Collectors 108 can be located on or off premises. It will be appreciated that the placement of collectors 108 can be optimized according to various priorities such as network capacity, cost, and system responsiveness. In some example embodiments, data storage of collectors 108 is located in an in-memory database, such as dashDB by International Business Machines. This approach benefits from rapid random access speeds that typically are required for analytics software. Alternatively, collectors 108 can utilize solid state drives, disk drives, magnetic tape drives, or a combination of the foregoing according to cost, responsiveness, and size requirements. Collectors 108 can utilize various database structures such as a normalized relational database or NoSQL database.

In some example embodiments, collectors 108 may only serve as network storage for network traffic monitoring system 100. In other example embodiments, collectors 108 can organize, summarize, and preprocess data. For example, collectors 108 can tabulate how often packets of certain sizes or types are transmitted from different nodes of a data center. Collectors 108 can also characterize the traffic flows going to and from various nodes. In some example embodiments, collectors 108 can match packets based on sequence numbers, thus identifying traffic flows and connection links. In some example embodiments, collectors 108 can flag anomalous data. Because it would be inefficient to retain all data indefinitely, in some example embodiments, collectors 108 can periodically replace detailed network traffic flow data and associated data (host data, process data, user data, etc.) with consolidated summaries. In this manner, collectors 108 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic flow data and associated data of other periods of time (e.g., day, week, month, year, etc.). By organizing, summarizing, and preprocessing the network traffic flow data and associated data, collectors 108 can help network traffic monitoring system 100 scale efficiently. Although collectors 108 are generally referred to herein in the plurality, it will be appreciated that collectors 108 can be implemented using a single machine, especially for smaller datacenters.

In some example embodiments, collectors 108 can receive data from external data sources 106, such as security reports, white-lists (106 a), IP watchlists (106 b), whois data (106 c), or out-of-band data, such as power status, temperature readings, etc.

In some example embodiments, network traffic monitoring system 100 can include a wide bandwidth connection between collectors 108 and analytics module 110. Analytics module 110 can include application dependency (ADM) module 160, reputation module 162, vulnerability module 164, malware detection module 166, etc., to accomplish various tasks with respect to the flow data and associated data collected by sensors 104 and stored in collectors 108. In some example embodiments, network traffic monitoring system 100 can automatically determine network topology. Using network traffic flow data and associated data captured by sensors 104, network traffic monitoring system 100 can determine the type of devices existing in the network (e.g., brand and model of switches, gateways, machines, etc.), physical locations (e.g., latitude and longitude, building, datacenter, room, row, rack, machine, etc.), interconnection type (e.g., 10 Gb Ethernet, fiber-optic, etc.), and network characteristics (e.g., bandwidth, latency, etc.). Automatically determining the network topology can assist with integration of network traffic monitoring system 100 within an already established datacenter. Furthermore, analytics module 110 can detect changes of network topology without the need of further configuration.

Analytics module 110 can determine dependencies of components within the network using ADM module 160. For example, if component A routinely sends data to component B but component B never sends data to component A, then analytics module 110 can determine that component B is dependent on component A, but A is likely not dependent on component B. If, however, component B also sends data to component A, then they are likely interdependent. These components can be processes, virtual machines, hypervisors, virtual local area networks (VLANs), etc. Once analytics module 110 has determined component dependencies, it can then form a component (“application”) dependency map. This map can be instructive when analytics module 110 attempts to determine a root cause of a failure (because failure of one component can cascade and cause failure of its dependent components). This map can also assist analytics module 110 when attempting to predict what will happen if a component is taken offline. Additionally, analytics module 110 can associate edges of an application dependency map with expected latency, bandwidth, etc. for that individual edge.

Analytics module 110 can establish patterns and norms for component behavior. For example, it can determine that certain processes (when functioning normally) will only send a certain amount of traffic to a certain VM using a small set of ports. Analytics module can establish these norms by analyzing individual components or by analyzing data coming from similar components (e.g., VMs with similar configurations). Similarly, analytics module 110 can determine expectations for network operations. For example, it can determine the expected latency between two components, the expected throughput of a component, response times of a component, typical packet sizes, traffic flow signatures, etc. In some example embodiments, analytics module 110 can combine its dependency map with pattern analysis to create reaction expectations. For example, if traffic increases with one component, other components may predictably increase traffic in response (or latency, compute time, etc.).

In some example embodiments, analytics module 110 can use machine learning techniques to identify security threats to a network using malware detection module 166. For example, malware detection module 166 can be provided with examples of network states corresponding to an attack and network states corresponding to normal operation. Malware detection module 166 can then analyze network traffic flow data and associated data to recognize when the network is under attack. In some example embodiments, the network can operate within a trusted environment for a time so that analytics module 110 can establish baseline normalcy. In some example embodiments, analytics module 110 can contain a database of norms and expectations for various components. This database can incorporate data from sources external to the network (e.g., external sources 106). Analytics module 110 can then create access policies for how components can interact using policy engine 112. In some example embodiments, policies can be established external to network traffic monitoring system 100 and policy engine 112 can detect the policies and incorporate them into analytics module 110. A network administrator can manually tweak the policies. Policies can dynamically change and be conditional on events. These policies can be enforced by the components depending on a network control scheme implemented by a network. Policy engine 112 can maintain these policies and receive user input to change the policies.

Policy engine 112 can configure analytics module 110 to establish or maintain network policies. For example, policy engine 112 may specify that certain machines should not intercommunicate or that certain ports are restricted. A network and security policy controller (not shown) can set the parameters of policy engine 112. In some example embodiments, policy engine 112 can be accessible via presentation module 116. In some example embodiments, policy engine 112 can include policy data 112. In some example embodiments, policy data 112 can include endpoint group (EPG) data 114, which can include the mapping of EPGs to IP addresses and/or MAC addresses. In some example embodiments, policy data 112 can include policies for handling data packets.

In some example embodiments, analytics module 110 can simulate changes in the network. For example, analytics module 110 can simulate what may result if a machine is taken offline, if a connection is severed, or if a new policy is implemented. This type of simulation can provide a network administrator with greater information on what policies to implement. In some example embodiments, the simulation may serve as a feedback loop for policies. For example, there can be a policy that if certain policies would affect certain services (as predicted by the simulation) those policies should not be implemented. Analytics module 110 can use simulations to discover vulnerabilities in the datacenter. In some example embodiments, analytics module 110 can determine which services and components will be affected by a change in policy. Analytics module 110 can then take necessary actions to prepare those services and components for the change. For example, it can send a notification to administrators of those services and components, it can initiate a migration of the components, it can shut the components down, etc.

In some example embodiments, analytics module 110 can supplement its analysis by initiating synthetic traffic flows and synthetic attacks on the datacenter. These artificial actions can assist analytics module 110 in gathering data to enhance its model. In some example embodiments, these synthetic flows and synthetic attacks are used to verify the integrity of sensors 104, collectors 108, and analytics module 110. Over time, components may occasionally exhibit anomalous behavior. Analytics module 110 can analyze the frequency and severity of the anomalous behavior to determine a reputation score for the component using reputation module 162. Analytics module 110 can use the reputation score of a component to selectively enforce policies. For example, if a component has a high reputation score, the component may be assigned a more permissive policy or more permissive policies; while if the component frequently violates (or attempts to violate) its relevant policy or policies, its reputation score may be lowered and the component may be subject to a stricter policy or stricter policies. Reputation module 162 can correlate observed reputation score with characteristics of a component. For example, a particular virtual machine with a particular configuration may be more prone to misconfiguration and receive a lower reputation score. When a new component is placed in the network, analytics module 110 can assign a starting reputation score similar to the scores of similarly configured components. The expected reputation score for a given component configuration can be sourced outside of the datacenter. A network administrator can be presented with expected reputation scores for various components before installation, thus assisting the network administrator in choosing components and configurations that will result in high reputation scores.

Some anomalous behavior can be indicative of a misconfigured component or a malicious attack. Certain attacks may be easy to detect if they originate outside of the datacenter, but can prove difficult to detect and isolate if they originate from within the datacenter. One such attack could be a distributed denial of service (DDOS) where a component or group of components attempt to overwhelm another component with spurious transmissions and requests. Detecting an attack or other anomalous network traffic can be accomplished by comparing the expected network conditions with actual network conditions. For example, if a traffic flow varies from its historical signature (packet size, transport control protocol header options, etc.) it may be an attack.

In some cases, a traffic flow and associated data may be expected to be reported by a sensor, but the sensor may fail to report it. This situation could be an indication that the sensor has failed or become compromised. By comparing the network traffic flow data and associated data from multiple sensors 104 spread throughout the datacenter, analytics module 110 can determine if a certain sensor is failing to report a particular traffic flow.

Presentation module 116 can include serving layer 118, authentication module 120, web front end 122, public alert module 124, and third party tools 126. In some example embodiments, presentation module 116 can provide an external interface for network monitoring system 100. Using presentation module 116, a network administrator, external software, etc. can receive data pertaining to network monitoring system 100 via a webpage, application programming interface (API), audiovisual queues, etc. In some example embodiments, presentation module 116 can preprocess and/or summarize data for external presentation. In some example embodiments, presentation module 116 can generate a webpage. As analytics module 110 processes network traffic flow data and associated data and generates analytic data, the analytic data may not be in a human-readable form or it may be too large for an administrator to navigate. Presentation module 116 can take the analytic data generated by analytics module 110 and further summarize, filter, and organize the analytic data as well as create intuitive presentations of the analytic data.

Serving layer 118 can be the interface between presentation module 116 and analytics module 110. As analytics module 110 generates reports, predictions, and conclusions, serving layer 118 can summarize, filter, and organize the information that comes from analytics module 110. In some example embodiments, serving layer 118 can also request raw data from a sensor or collector.

Web frontend 122 can connect with serving layer 118 to present the data from serving layer 118 in a webpage. For example, web frontend 122 can present the data in bar charts, core charts, tree maps, acyclic dependency maps, line graphs, tables, etc. Web frontend 122 can be configured to allow a user to “drill down” on information sets to get a filtered data representation specific to the item the user wishes to drill down to. For example, individual traffic flows, components, etc. Web frontend 122 can also be configured to allow a user to filter by search. This search filter can use natural language processing to analyze the user's input. There can be options to view data relative to the current second, minute, hour, day, etc. Web frontend 122 can allow a network administrator to view traffic flows, application dependency maps, network topology, etc.

In some example embodiments, web frontend 122 may be solely configured to present information. In other example embodiments, web frontend 122 can receive inputs from a network administrator to configure network traffic monitoring system 100 or components of the datacenter. These instructions can be passed through serving layer 118 to be sent to configuration and image manager 102 or policy engine 112. Authentication module 120 can verify the identity and privileges of users. In some example embodiments, authentication module 120 can grant network administrators different rights from other users according to established policies.

Public alert module 124 can identify network conditions that satisfy specified criteria and push alerts to third party tools 126. Public alert module 124 can use analytic data generated or accessible through analytics module 110. One example of third party tools 126 is a security information and event management system (SIEM). Third party tools 126 may retrieve information from serving layer 118 through an API and present the information according to the SIEM's user interfaces.

FIG. 2 illustrates an example network environment 200 according to some example embodiments. It should be understood that, for the network environment 100 and any environment discussed herein, there can be additional or fewer nodes, devices, links, networks, or components in similar or alternative configurations. Example embodiments with different numbers and/or types of clients, networks, nodes, cloud components, servers, software components, devices, virtual or physical resources, configurations, topologies, services, appliances, deployments, or network devices are also contemplated herein. Further, network environment 200 can include any number or type of resources, which can be accessed and utilized by clients or tenants. The illustrations and examples provided herein are for clarity and simplicity.

Network environment 200 can include network fabric 212, layer 2 (L2) network 206, layer 3 (L3) network 208, endpoints 210 a, 210 b, . . . , and 210 d (collectively, “204”). Network fabric 212 can include spine switches 202 a, 202 b, . . . , 202 n (collectively, “202”) connected to leaf switches 204 a, 204 b, 204 c, . . . , 204 n (collectively, “204”). Spine switches 202 can connect to leaf switches 204 in network fabric 212. Leaf switches 204 can include access ports (or non-fabric ports) and fabric ports. Fabric ports can provide uplinks to spine switches 202, while access ports can provide connectivity for devices, hosts, endpoints, VMs, or other electronic devices (e.g., endpoints 204), internal networks (e.g., L2 network 206), or external networks (e.g., L3 network 208).

Leaf switches 204 can reside at the edge of network fabric 212, and can thus represent the physical network edge. In some cases, leaf switches 204 can be top-of-rack switches configured according to a top-of-rack architecture. In other cases, leaf switches 204 can be aggregation switches in any particular topology, such as end-of-row or middle-of-row topologies. Leaf switches 204 can also represent aggregation switches, for example.

Network connectivity in network fabric 212 can flow through leaf switches 204. Here, leaf switches 204 can provide servers, resources, VMs, or other electronic devices (e.g., endpoints 210), internal networks (e.g., L2 network 206), or external networks (e.g., L3 network 208), access to network fabric 212, and can connect leaf switches 204 to each other. In some example embodiments, leaf switches 204 can connect endpoint groups (EPGs) to network fabric 212, internal networks (e.g., L2 network 206), and/or any external networks (e.g., L3 network 208). EPGs can be used in network environment 200 for mapping applications to the network. In particular, EPGs can use a grouping of application endpoints in the network to apply connectivity and policy to the group of applications. EPGs can act as a container for buckets or collections of applications, or application components, and tiers for implementing forwarding and policy logic. EPGs also allow separation of network policy, security, and forwarding from addressing by instead using logical application boundaries. For example, each EPG can connect to network fabric 212 via leaf switches 204.

Endpoints 210 can connect to network fabric 212 via leaf switches 204. For example, endpoints 210 a and 210 b can connect directly to leaf switch 204 a, which can connect endpoints 210 a and 210 b to network fabric 212 and/or any other one of leaf switches 204. Endpoints 210 c and 210 d can connect to leaf switch 204 b via L2 network 206. Endpoints 210 c and 210 d and L2 network 206 are examples of LANs. LANs can connect nodes over dedicated private communications links located in the same general physical location, such as a building or campus.

Wide area network (WAN) 212 can connect to leaf switches 204 c or 204 d via L3 network 208. WANs can connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links. LANs and WANs can include layer 2 (L2) and/or layer 3 (L3) networks and endpoints.

The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol can refer to a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective size of each network. Endpoints 210 can include any communication device or component, such as a computer, server, hypervisor, virtual machine, container, process (e.g., running on a virtual machine), switch, router, gateway, host, device, external network, etc. In some example embodiments, endpoints 210 can include a server, hypervisor, process, or switch configured with virtual tunnel endpoint (VTEP) functionality which connects an overlay network with network fabric 212. The overlay network may allow virtual networks to be created and layered over a physical network infrastructure. Overlay network protocols, such as Virtual Extensible LAN (VXLAN), Network Virtualization using Generic Routing Encapsulation (NVGRE), Network Virtualization Overlays (NVO3), and Stateless Transport Tunneling (STT), can provide a traffic encapsulation scheme which allows network traffic to be carried across L2 and L3 networks over a logical tunnel. Such logical tunnels can be originated and terminated through VTEPs. The overlay network can host physical devices, such as servers, applications, endpoint groups, virtual segments, virtual workloads, etc. In addition, endpoints 210 can host virtual workload(s), clusters, and applications or services, which can connect with network fabric 212 or any other device or network, including an internal or external network. For example, endpoints 210 can host, or connect to, a cluster of load balancers or an EPG of various applications.

Network environment 200 can also integrate a network traffic monitoring system, such as the one shown in FIG. 1. For example, as shown in FIG. 2, the network traffic monitoring system can include sensors 104 a, 104 b, . . . , 104 n (collectively, “104”), collectors 108 a, 108 b, . . . 108 n (collectively, “108”), and analytics module 110. In some example embodiments, spine switches 202 do not have sensors 104. Analytics module 110 can receive and process network traffic and associated data collected by collectors 108 and detected by sensors 104 placed on nodes located throughout network environment 200. In some example embodiments, analytics module 110 can be implemented in an active-standby model to ensure high availability, with a first analytics module functioning in a primary role and a second analytics module functioning in a secondary role. If the first analytics module fails, the second analytics module can take over control. Although analytics module 110 is shown to be a standalone network appliance in FIG. 2, it will be appreciated that analytics module 110 can also be implemented as a VM image that can be distributed onto a VM, a cluster of VMs, a software as a service (SaaS), or other suitable distribution model in various other example embodiments. In some example embodiments, sensors 104 can run on endpoints 210, leaf switches 204, spine switches 202, in-between network elements (e.g., sensor 104 h), etc. In some example embodiments, leaf switches 204 can each have an associated collector 108. For example, if leaf switch 204 is a top of rack switch then each rack can contain an assigned collector 108.

Although network fabric 212 is illustrated and described herein as an example leaf-spine architecture, one of ordinary skill in the art will readily recognize that the subject technology can be implemented based on any network topology, including any data center or cloud network fabric. Indeed, other architectures, designs, infrastructures, and variations are contemplated herein. For example, the principles disclosed herein are applicable to topologies including three-tier (including core, aggregation, and access levels), fat tree, mesh, bus, hub and spoke, etc. It should be understood that sensors and collectors can be placed throughout the network as appropriate according to various architectures.

FIG. 3 illustrates an example method according to some embodiments. A system such as network monitoring system 100 can perform the example method of FIG. 3. The system can begin and detect, using a sensor installed on an endpoint, a plurality of flows associated with the endpoint (step 302). The endpoint can be a virtual machine, container, user account, application, machine, switch, router, firewall, endpoint group, location, etc. The sensor can be sensor 104 and can be installed on the endpoint or on another endpoint whereby the sensor can monitor the flows to and from the endpoint. In some embodiments, the sensor records the header data of any packet or flow that passes with the sensor. The plurality of flows can be from or to the endpoint and can include a plurality of other endpoints (e.g., the endpoint can send and receive flows from multiple other endpoints). Because some flows might be sent between two endpoints residing on a single host machine, sensors that are installed on each endpoint (e.g., each virtual machine) can be effective to detect the flows whereas other intra-datacenter sensors might not be able to detect the flows.

The system can then determine an entropy associated with a header field for the plurality of flows (step 304). Each flow can have one or more packets. A flow does not necessarily describe all possible packets for a communication. A packet can be a grouping of data. Each packet can have one or more header data. For example, a packet can have an Ethernet header, an Internet Protocol (IP) header, a Transport Control Protocol (TCP) header, an application header, etc. The sensor can detect and use any of these headers, regardless of the placement of the sensor. For example, although a switch might only be concerned with the Ethernet Header a sensor installed on the switch can analyze the application header.

Each header has header fields that help with routing and management of the associated packet. These fields can include Ethernet header fields (e.g., source media access control-MAC-address, destination MAC address), IP header fields (e.g., total length, identification, flags, fragment offset, time to live, protocol, header checksum, source IP address, destination IP address, options), TCP header fields (e.g., source port, destination port, sequence number, acknowledgement number, window, TCP options), data header fields, etc. An attacking endpoint may try to get a victim endpoint (e.g., the endpoint of step 302) to run malicious code, become overloaded, or receive command and control instructions (e.g., if the victim endpoint is discretely running malicious code). For example, a malicious endpoint may use reserved or underutilized header fields to communicate a message to a hidden program on the victim endpoint. A malicious endpoint might attempt to intercept a communication and send incorrect data (e.g., by guessing correct parameters for an active flow, such as the sequence number).

Some header fields generally have predictable values. For example, source and destination ports will likely comprise a small number of values corresponding to various applications running on the endpoint. Thus, after observing the values over time, they will show certain characteristics such as a median, range, variance, etc. The sequence number can predictably increase; for example, a first packet will have a sequence number and a packet length, the next packet will have a sequence number that is equal to the original number plus the length. An attacker might attempt to guess the correct sequence number and will send packets with different sequence numbers.

The predictability of header field values can be informed by the current flow, past flows to the endpoint, similar flows (e.g., to similar endpoints), etc.

Entropy can be how unpredictable a value is. For example, a header field might exhibit a certain variance, maximum and minimum, average, linear increase, etc. and if a flow has header field values that deviate from that predictability, it can be said that the flow has a high entropy in that header field. A header field value that is very predictable can be said to have low entropy. Calculating entropy can include a temporal element. For example, a header flag that is typically detected at a regular frequency can have a lower entropy than a situation when the header flag is detected at an irregular frequency. Multiple header fields can be analyzed in combination for a combined header entropy. For example, two header fields might be strongly correlated. If the two header fields diverge and no longer are correlated as strongly, the entropy can increase.

The system can then detect a second plurality of flows (step 306). The second plurality of flows can be associated with the endpoint of step 302, or another endpoint. In some embodiments, the second plurality of flows is detected using a sensor installed on the endpoint of step 302.

The system can then determine a second entropy associated with the header field for the second plurality of flows (step 310). Step 310 can be similar to step 304. The system can then determine whether the entropy (e.g., of step 304) is greater than a predetermined amount (step 312). The predetermined amount can be the second plurality of flows. For example, the second plurality of flows can be associated with legitimate (e.g., normal) flows and can be used as a control. Thus, flows that have a higher entropy can be considered anomalous or likely malicious. In some embodiments, the predetermined amount is some value greater than the second entropy to allow a buffer.

If the entropy is less than the predetermined amount, the system can determine that the plurality of flows is normal (step 314). That is, the system can label the plurality of flows as benign or legitimate. Alternatively, if the entropy is greater than the predetermined amount, the system can determine that the plurality of flows is anomalous (step 316).

In some embodiments, the system can use machine learning to determine what header fields and what entropy values are indicative of legitimate or malicious flows. For example, the system can be provided with multiple flows that have been pre-labeled as legitimate or malicious. The system can then determine the entropy for various fields of the various flows. When benign flows typically have less entropy in a field than malicious flows, the system can determine that that field is a good indicator for malicious flows. It can then set a cutoff automatically at what entropy level would indicate a malicious flow.

Some header fields are should have a certain value. For example, the identification field of an IPv4 header is generally not used and should be left at a default value. Some malicious flows attempt to exploit the identification field to pass data to a subservient endpoint without being detected. Thus, an identification field that is not the default might be malicious.

FIG. 4 illustrates an example graph of the count of various Time to Live values for flows. According to some embodiments, the time to live header value for flows can have a predictable normal distribution 302. Line 402 can represent a distribution of legitimate flows. A malicious endpoint might attempt to try various time to live values. Line 404 can represent the various values that a malicious endpoint may send out. Because line 404 exhibits greater entropy than line 402, the system can determine that the flows and endpoint(s) associated with it might be malicious.

FIG. 5 illustrates an example graph of sequence numbers for various flows. For example, line 504 can represent one flow and how the sequence numbers get incremented with regularity. Line 502 can represent malicious packets that are sent attempting to guess the correct sequence number. Because line 504 shows more regularity than line 502, it can be said that line 504 has less entropy and line 502 is likely associated with a malicious flow and endpoint.

Port header field entropy can be used to identify anomalous flows. For example, an endpoint might have a limited number of ports open corresponding with the applications running on the endpoint. In an attempt to find an open port, a malicious endpoint might attempt to send flows to a variety of ports. A malicious or misconfigured endpoint might send flows to a port that corresponds to an application that is not running on the target endpoint or is running on the endpoint but should not be receiving data. Because of a misconfigured firewall, such flows might be successful in exploiting a vulnerable endpoint. Thus, it can be important to flag flows that exhibit greater port entropy.

Port header field entropy can also identify flows designed as “port knocks.” A port knock is where a malicious endpoint seeks to send a command to a malicious application running on a target node. Even though the target nodes might block most ports, the malicious endpoint can send flows to a sequence of ports, the target endpoint will then block the flows and the malicious application can read the firewall report on the endpoint and determine which flows were blocked to which ports. The malicious application can then translate the firewall report into a command message. Analyzing port header field entropy can identify such port knocks, which can be useful even if the ports are blocked by the endpoint.

Source address header field entropy can be used to indicate that a distributed denial of service attack is occurring. For example, multiple endpoints can be creating spurious flows sent to a single endpoint. The system can identify that typically a small collection of source addresses communicate with the endpoint but that a plurality of flows have a large number of different source addresses. This can be indicative of an attack. The legitimate flows might correspond with a specific address group and the attacking flows might not correspond to the address group. Source address can refer to MAC address or IP address.

Source address or destination address header field entropy can be used to identify an overload attack on a router, switch, or other networking apparatus. For example, an endpoint might be required to keep track of addresses (IP or MAC) in order to perform routing (or other) functions. A malicious endpoint can then send a variety of flows with various addresses in order to overload the memory of the endpoint that is keeping track of such addresses. Analyzing address field entropy can help identify such attacks.

Similarly, syn flooding is a technique that attackers use to try to overload network resources. Thus, the system can determine whether syn responses follow a regular pattern or are anomalous. In similar manner, the system can check to see if flows exhibit characteristics of a TCP fragmentation attack.

Internet Message Control Protocol (IMCP) echo requests can be identified in the associated header field. A malicious endpoint might use these echo requests to overwhelm a network or endpoint. The system can identify if the IMCP echo requests exhibit greater entropy than is typically observed. For example, such requests may typically come at regular intervals; during an attack they may come at random or otherwise irregular intervals.

Source address header field entropy can help identify a spoofing attack. For example, a certain endpoint (such as a switch or router) might typically not see flows from a certain address. A malicious endpoint might attempt to spoof (i.e., imitate) another endpoint on the network by providing that endpoint's address as its own (i.e., the malicious endpoint's). The switch can then determine that flows have a higher source address header field entropy because they contain the spoofed address. Such flows can be flagged as malicious.

In some embodiments, a network environment only uses a certain set of protocols. Flows that are outside that set of protocols might be malicious and used for an attack such as a distributed denial of service attack. The system can thus analyze the protocol header field to determine if the entropy has increased to see if the flows might be malicious. An example of protocols can be UDP and TCP.

Header field entropy can be monitored in combination. For example, two header fields may exhibit normal entropy independently, but they typically are correlated to each other. For example, a certain type of flow might have a certain packet length. A malicious collection of flows may have a normal entropy of lengths, but they are uncorrelated with the certain type of flow.

FIG. 6A and FIG. 6B illustrate example system embodiments. The more appropriate embodiment will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system embodiments are possible.

FIG. 6A illustrates a conventional system bus computing system architecture 600 wherein the components of the system are in electrical communication with each other using a bus 605. Example system 600 includes a processing unit (CPU or processor) 610 and a system bus 605 that couples various system components including the system memory 615, such as read only memory (ROM) 670 and random access memory (RAM) 675, to the processor 610. The system 600 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 610. The system 600 can copy data from the memory 615 and/or the storage device 630 to the cache 612 for quick access by the processor 610. In this way, the cache can provide a performance boost that avoids processor 610 delays while waiting for data. These and other modules can control or be configured to control the processor 610 to perform various actions. Other system memory 615 may be available for use as well. The memory 615 can include multiple different types of memory with different performance characteristics. The processor 610 can include any general purpose processor and a hardware module or software module, such as module 1 637, module 7 634, and module 3 636 stored in storage device 630, configured to control the processor 910 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 610 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction with the computing device 600, an input device 645 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 635 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 600. The communications interface 640 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 630 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 675, read only memory (ROM) 670, and hybrids thereof.

The storage device 630 can include software modules 637, 634, 636 for controlling the processor 610. Other hardware or software modules are contemplated. The storage device 630 can be connected to the system bus 605. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 610, bus 605, display 635, and so forth, to carry out the function.

FIG. 6B illustrates an example computer system 650 having a chipset architecture that can be used in executing the described method and generating and displaying a graphical user interface (GUI). Computer system 650 is an example of computer hardware, software, and firmware that can be used to implement the disclosed technology. System 650 can include a processor 655, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 655 can communicate with a chipset 660 that can control input to and output from processor 655. In this example, chipset 660 outputs information to output 665, such as a display, and can read and write information to storage device 670, which can include magnetic media, and solid state media, for example. Chipset 660 can also read data from and write data to RAM 675. A bridge 680 for interfacing with a variety of user interface components 685 can be provided for interfacing with chipset 660. Such user interface components 685 can include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to system 650 can come from any of a variety of sources, machine generated and/or human generated.

Chipset 660 can also interface with one or more communication interfaces 690 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein can include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 655 analyzing data stored in storage 670 or 675. Further, the machine can receive inputs from a user via user interface components 685 and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 655.

It can be appreciated that example systems 600 and 650 can have more than one processor 610 or be part of a group or cluster of computing devices networked together to provide greater processing capability.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. Moreover, claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim. 

The invention claimed is:
 1. A computer-implemented method comprising: detecting, via one or more sensors, a plurality of flows; determining a plurality of entropies associated with the plurality of flows wherein the entropies are associated with header data of packets of the plurality of flows; determining, based on previous flows within a previous time period, an amount of traffic indicating non-malicious traffic in the previous flows; determining whether an entropy of the plurality of entropies is greater than the amount of traffic indicating non-malicious traffic; in response to the entropy of the plurality of entropies being greater than the amount of traffic indicating non-malicious traffic, determining an associated flow of the plurality of flows is anomalous; and in response to the entropy of the plurality of entropies being less than or equal to the amount, determining the associated flow of the plurality of flows is normal.
 2. The computer-implemented method of claim 1, further comprising: provide a buffer when the amount is greater than a second entropy of the plurality of entropies.
 3. The computer-implemented method of claim 1, wherein, the plurality of flows includes a portion of flows detected via a first sensor of the one or more sensors installed on a first endpoint, the first endpoint is a destination for the portion of flows, the detecting of the plurality of flows includes detecting, via a second sensor of the one or more sensors associated with a second endpoint, the portion of flows, and the second endpoint is a source for the portion of flows.
 4. The computer-implemented method of claim 1, wherein, at least one of the plurality of entropies is associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a packet identification field of Internet Protocol version 4 standard.
 5. The computer-implemented method of claim 1, wherein at least one of the plurality of entropies includes associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a time to live field and/or a sequence identifier field.
 6. The computer-implemented method of claim 1, wherein the determining of the plurality of entropies includes determining an expected sequence identifier field.
 7. The computer-implemented method of claim 6, wherein the determining of the plurality of entropies includes determining a difference between the expected sequence identifier field and a detected sequence identifier field.
 8. A non-transitory computer-readable medium having computer readable instructions that, when executed by a processor of a computer, cause the computer to: detect, via one or more sensors, a plurality of flows; determine a plurality of entropies associated with the plurality of flows wherein the entropies are associated with header data of packets of the plurality of flows; determine, based on previous flows within a previous time period, an amount of traffic indicating non-malicious traffic in the previous flows; determine whether an entropy of the plurality of entropies is greater than the amount of traffic indicating non-malicious traffic; in response to the entropy of the plurality of entropies being greater than the amount of traffic indicating non-malicious traffic, determine the one of the plurality of flows is anomalous; and in response to the entropy of the plurality of entropies is less than or equal to the amount, determine the one of the plurality of flows is normal.
 9. The non-transitory computer-readable medium of claim 8, wherein the instructions further cause the computer to: provide a buffer when the amount is greater than a second entropy of the plurality of entropies.
 10. The non-transitory computer-readable medium of claim 8, wherein, the plurality of flows includes a portion of flows detected via a first sensor of the one or more sensors installed on a first endpoint, the first endpoint is a destination for the portion of flows, detecting the plurality of flows includes detecting, via a second sensor of the one or more sensors associated with a second endpoint, the portion of flows, and the second endpoint is a source for the portion of flows.
 11. The non-transitory computer-readable medium of claim 8, wherein, at least one of the plurality of entropies is associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a packet identification field of Internet Protocol version 4 standard.
 12. The non-transitory computer-readable medium of claim 8, wherein, at least one of the plurality of entropies is associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a time to live field and/or a sequence identifier field.
 13. The non-transitory computer-readable medium of claim 8, wherein determining the plurality of entropies includes determining an expected sequence identifier field.
 14. The non-transitory computer-readable medium of claim 13, wherein determining the plurality of entropies includes determining a difference between the expected sequence identifier field and a detected sequence identifier field.
 15. A system comprising: a processor; a memory including instructions that when executed by the processor, cause the system to: detect, via one or more sensors, a plurality of flows; determine a plurality of entropies associated with the plurality of flows wherein the entropies are associated with header data of packets of the plurality of flows; determine, based on previous flows within a previous time period, an amount of traffic indicating non-malicious traffic in the previous flows; determine whether an entropy of the plurality of entropies is greater than the amount of traffic indicating non-malicious traffic; in response to the entropy of the plurality of entropies is greater than the amount of traffic indicating non-malicious traffic, determine the one of the plurality of flows is anomalous; and in response to the entropy of the plurality of entropies is less than or equal to the amount, determine the one of the plurality of flows is normal.
 16. The system of claim 15, wherein the instructions further cause the system to: provide a buffer when the amount is greater than a second entropy of the plurality of entropies.
 17. The system of claim 15, wherein, the plurality of flows includes a portion of flows detected via a first sensor of the one or more sensors installed on a first endpoint, the first endpoint is a destination for the portion of flows, detecting the plurality of flows includes detecting, via a second sensor of the one or more sensors associated with a second endpoint, the portion of flows, and the second endpoint is a source for the portion of flows.
 18. The system of claim 15, wherein, at least one of the plurality of entropies is associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a packet identification field of Internet Protocol version 4 standard.
 19. The system of claim 15, wherein, at least one of the plurality of entropies is associated with at least one of a plurality of header fields, the plurality of header fields have various entropy values, and the at least one of the plurality of header fields is a time to live field and/or a sequence identifier field.
 20. The system of claim 15, wherein, determining the plurality of entropies includes determining an expected sequence identifier field, and determining the plurality of entropies includes determining a difference between the expected sequence identifier field and a detected sequence identifier field. 